A simple HPLC method was developed for determination of quercitrin and isoquercitrin in rat plasma. Reversed-phase HPLC was employed for the quantitative analysis using kaempferol-3-O--d-glucopyranoside-7-O--l-rhamnoside as an internal standard. Following extraction from the plasma samples with ethyl acetate-isopropanol (95:5, v/v), these two compounds were successfully separated on a Luna C18 column (250 × 4.6 mm, 5 µm) with isocratic elution of acetonitrile-0.5% aqueous acetic acid (17:83, v/v) as the mobile phase. The flow-rate was set at 1 mL/min and the eluent was detected at 350 nm for both quercitrin and isoquercitrin. The method was linear over the studied ranges of 50-6000 and 50-5000 ng/mL for quercitrin and isoquercitrin, respectively. The intra- and inter-day precisions of the analysis were better than 13.1 and 13.2%, respectively. The lower limits of quantitation for quercitrin and isoquercitrin in plasma were both of 50 ng/mL. The mean extraction recoveries were 73 and 61% for quercitrin and i
soquercitrin, respectively. The validated method was successfully applied to pharmacokinetic studies of the two analytes in rat plasma after the oral administration of Hypericum japonicum thunb. ethanol extract.

In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions. We describe different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches. Advantages and disadvantages of the different spectral clustering algorithms are discussed.

The abilities to learn and to categorize are fundamental for cognitive systems, be it animals or machines, and therefore have attracted attention from engineers and psychologists alike. Modern machine learning methods and psychological models of categorization are remarkably similar, partly because these two fields share a common history in artificial neural networks and reinforcement learning. However, machine learning is now an independent and mature field that has moved beyond psychologically or neurally inspired algorithms towards providing foundations for a theory of learning that is rooted in statistics and functional analysis. Much of this research is potentially interesting for psychological theories of learning and categorization but also hardly accessible for psychologists. Here, we provide a tutorial introduction to a popular class of machine learning tools, called kernel methods. These methods are closely related to perceptrons, radial-basis-function neural networks and exemplar theories of catego
rization. Recent theoretical advances in machine learning are closely tied to the idea that the similarity of patterns can be encapsulated in a positive definite kernel. Such a positive definite kernel can define a reproducing kernel Hilbert space which allows one to use powerful tools from functional analysis for the analysis of learning algorithms. We give basic explanations of some key conceptsthe so-called kernel trick, the representer theorem and regularizationwhich may open up the possibility that insights from machine learning can feed back into psychology.

We prove asymptotic stability of the solutions of equations describing a simple queueing system consisting of two machines separated by a finite storage buffer. Following an approach by G. Gupur, we apply the theory of C0-semigroups and spectral theory of positive operators.

One knows the imaging system's properties are central to the correct interpretation of any image. In a scanning electron microscope regions of different composition generally interact in a highly nonlinear way during signal generation. Using Monte Carlo simulations we found that in resin-embedded, heavy metal-stained biological specimens staining is sufficiently dilute to allow an approximately linear treatment. We then mapped point-spread functions for backscattered-electron contrast, for primary energies of 3 and 7 keV and for different detector specifications. The point-spread functions are surprisingly well confined (both laterally and in depth) compared even to the distribution of only those scattered electrons that leave the sample again.

Background: For splice site recognition, one has to solve two classification problems:
discriminating true from decoy splice sites for both acceptor and donor sites. Gene finding systems
typically rely on Markov Chains to solve these tasks.
Results: In this work we consider Support Vector Machines for splice site recognition. We employ
the so-called weighted degree kernel which turns out well suited for this task, as we will illustrate in
several experiments where we compare its prediction accuracy with that of recently proposed
systems. We apply our method to the genome-wide recognition of splice sites in Caenorhabditis
elegans, Drosophila melanogaster, Arabidopsis thaliana, Danio rerio, and Homo sapiens. Our
performance estimates indicate that splice sites can be recognized very accurately in these genomes
and that our method outperforms many other methods including Markov Chains, GeneSplicer and
SpliceMachine. We provide genome-wide predictions of splice sites and a stand-alone prediction
tool ready to be used for incorporation in a gene finder.
Availability: Data, splits, additional information on the model selection, the whole genome
predictions, as well as the stand-alone prediction tool are available for download at http://
www.fml.mpg.de/raetsch/projects/splice.

Recently, Udwadia (Proc. R. Soc. Lond. A 2003:17831800, 2003) suggested to derive tracking controllers for mechanical systems with redundant degrees-of-freedom (DOFs) using a generalization of Gauss principle of least constraint. This method allows reformulating control problems as a special class of optimal controllers. In this paper, we take this line of reasoning one step further and demonstrate that several well-known and also novel nonlinear robot control laws can be derived from this generic methodology. We show experimental verifications on a Sarcos Master Arm robot for some of the derived controllers. The suggested approach offers a promising unification and simplification of nonlinear control law design for robots obeying rigid body dynamics equations, both with or without external constraints, with over-actuation or underactuation, as well as open-chain and closed-chain kinematics.

Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful learning algorithms for diverse applications. However, the true potential of these methods is not realized, since existing implementations are not openly shared, resulting in software with low usability, and weak interoperability. We argue that this situation can be significantly improved by increasing incentives for researchers to publish their software under an open source model. Additionally, we outline the problems authors are faced with when trying to publish algorithmic implementations of machine learning methods. We believe that a resource of peer reviewed software accompanied by short articles would be highly valuable to both the machine learning and the general scientific community.

Support Vector Regression (SVR) for discrete data is considered. An alternative formulation of the representer theorem is derived. This result is based on the newly introduced notion of pseudoresidual and the use of subdifferential calculus. The representer theorem is exploited to analyze the sensitivity properties of ε-insensitive SVR and introduce the notion of approximate degrees of freedom. The degrees of freedom are shown to play a key role in the evaluation of the optimism, that is the difference between the expected in-sample error and the expected empirical risk. In this way, it is possible to define a Cp-like statistic that can be used for tuning the parameters of SVR. The proposed tuning procedure is tested on a simulated benchmark problem and on a real world problem (Boston Housing data set).

Journal of the Optical Society of America A, 24(10):3233-3241, October 2007 (article)

Abstract

There are 8 cycle / deg ripples or oscillations in performance as a function of location near Mach bands in experiments measuring Mach bands masking effects on random polarity signal bars. The oscillations with increments are 180 degrees out of phase with those for decrements. The oscillations, much larger than the measurement error, appear to relate to the weighting function of the spatial-frequency-tuned channel detecting the broad-
band signals. The ripples disappear with step maskers and become much smaller at durations below 25 ms, implying either that the site of masking has changed or that the weighting function and hence spatial-frequency tuning is slow to develop.

Human immunodeficiency virus type 1 (HIV-1) evolves in human body,
and its exposure to a drug often causes mutations that enhance
the resistance against the drug.
To design an effective pharmacotherapy for an individual patient,
it is important to accurately predict the drug resistance
based on genotype data.
Notably, the resistance is not just
the simple sum of the effects of all mutations.
Structural biological studies suggest that
the association of mutations is crucial:
Even if mutations A or B alone do not affect the resistance,
a significant change might happen
when the two mutations occur together.
Linear regression methods cannot take the associations into account,
while decision tree methods can reveal only limited associations.
Kernel methods and neural networks implicitly use all possible
associations for prediction, but cannot select salient associations
explicitly.
Our method, itemset boosting, performs linear regression
in the complete space of power sets of mutations.
It implements a forward feature selection procedure where,
in each iteration, one mutation combination is
found by an efficient branch-and-bound search.
This method uses all possible combinations,
and salient associations are explicitly shown.
In experiments, our method worked particularly well for predicting the
resistance of nucleotide reverse transcriptase inhibitors
(NRTIs). Furthermore, it successfully recovered many mutation
associations known in biological literature.

Electrophysiological signals of the developing fetal brain and heart can be investigated by fetal magnetoencephalography (fMEG). During such investigations, the fetal heart activity and that of the mother should be monitored continuously to provide an important indication of current well-being. Due to physical constraints of an fMEG system, it is not possible to use clinically established heart monitors for this purpose. Considering this constraint, we developed a real-time heart monitoring system for biomagnetic measurements and showed its reliability and applicability in research and for clinical examinations. The developed system consists of real-time access to fMEG data, an algorithm based on Independent Component Analysis (ICA), and a graphical user interface (GUI). The algorithm extracts the current fetal and maternal heart signal from a noisy and artifact-contaminated data stream in real-time and is able to adapt automatically to continuously varying environmental parameters. This algorithm has been na
med Adaptive Real-time ICA (ARICA) and is applicable to real-time artifact removal as well as to related blind signal separation problems.

Independent Component Analysis (ICA) designed for complete bases is used in a variety of applications with great success, despite the often questionable assumption of having N sensors and M sources with N&#8805;M. In this article, we assume a source model with more sources than sensors (M>N), only L<N of which are assumed to have a non-Gaussian distribution. We argue that this is a realistic source model for a variety of applications, and prove that for ICA algorithms designed for complete bases (i.e., algorithms assuming N=M) based on mutual information the mixture coefficients of the L non-Gaussian sources can be reconstructed in spite of the overcomplete mixture model. Further, it is shown that the reconstructed temporal activity of non-Gaussian sources is arbitrarily mixed with Gaussian sources. To obtain estimates of the temporal activity of the non-Gaussian sources, we use the correctly reconstructed mixture coefficients in conjunction with linearly
constrained minimum variance spatial filtering. This results in estimates of the non-Gaussian sources minimizing the variance of the interference of other sources. The approach is applied to the denoising of Event Related Fields recorded by MEG, and it is shown that it performs superiorly to ordinary ICA.

The final properties of sophisticated products can
be affected by many unapparent dependencies within the manufacturing
process, and the products integrity can often only be
checked in a final measurement. Troubleshooting can therefore
be very tedious if not impossible in large assembly lines.
In this paper we show that Feature Selection is an efficient tool for
serial-grouped lines to reveal causes for irregularities in product
attributes. We compare the performance of several methods for
Feature Selection on real-world problems in mass-production of
semiconductor devices.
Note to Practitioners We present a data based procedure
to localize flaws in large production lines: using the results of
final quality inspections and information about which machines
processed which batches, we are able to identify machines which
cause low yield.

Motivation: Identifying significant genes among thousands of sequences on a microarray is a central challenge for cancer research in bioinformatics. The ultimate goal is to detect the genes that are involved in disease outbreak and progression. A multitude of methods have been proposed for this task of feature selection, yet the selected gene lists differ greatly between different methods. To accomplish biologically meaningful gene selection from microarray data, we have to understand the theoretical connections and the differences between these methods. In this article, we define a kernel-based framework for feature selection based on the Hilbert–Schmidt independence criterion and backward elimination, called BAHSIC. We show that several well-known feature selectors are instances of BAHSIC, thereby clarifying their relationship. Furthermore, by choosing a different kernel, BAHSIC allows us to easily define novel feature selection algorithms. As a further advantage, feature selection via BAHSIC works directly on multiclass problems.
Results: In a broad experimental evaluation, the members of the BAHSIC family reach high levels of accuracy and robustness when compared to other feature selection techniques. Experiments show that features selected with a linear kernel provide the best classification performance in general, but if strong non-linearities are present in the data then non-linear kernels can be more suitable.

The cDNA array technology is a powerful tool to analyze a high number of genes in parallel. We investigated whether large-scale gene expression analysis allows clustering and identification of cellular phenotypes of chondrocytes in different in vivo and in vitro conditions. In 100% of cases, clustering analysis distinguished between in vivo and in vitro samples, suggesting fundamental differences in chondrocytes in situ and in vitro regardless of the culture conditions or disease status. It also allowed us to differentiate between healthy and osteoarthritic cartilage. The clustering also revealed the relative importance of the investigated culturing conditions (stimulation agent, stimulation time, bead/monolayer). We augmented the cluster analysis with a statistical search for genes showing differential expression. The identified genes provided hints to the molecular basis of the differences between the sample classes. Our approach shows the power of modern bioinformatic algorithms for understanding and class
ifying chondrocytic phenotypes in vivo and in vitro. Although it does not generate new experimental data per se, it provides valuable information regarding the biology of chondrocytes and may provide tools for diagnosing and staging the osteoarthritic disease process.

The genomes of individuals from the same species vary in sequence as a result of different evolutionary processes. To examine the patterns of, and the forces shaping, sequence variation in Arabidopsis thaliana, we performed high-density array resequencing of 20 diverse strains (accessions). More than 1 million nonredundant single-nucleotide polymorphisms (SNPs) were identified at moderate false discovery rates (FDRs), and ~4% of the genome was identified as being highly dissimilar or deleted relative to the reference genome sequence. Patterns of polymorphism are highly nonrandom among gene families, with genes mediating interaction with the biotic environment having exceptional polymorphism levels. At the chromosomal scale, regional variation in polymorphism was readily apparent. A scan for recent selective sweeps revealed several candidate regions, including a notable example in which almost all variation was removed in a 500-kilobase window. Analyzing the polymorphisms we describe in larger sets of accessions will enable a detailed understanding of forces shaping population-wide sequence variation in A. thaliana.

Given a sample from a probability measure with support on a submanifold in Euclidean space one can construct a neighborhood graph which can be seen as an approximation of the submanifold. The graph Laplacian of such a graph is used in several machine learning methods like semi-supervised learning, dimensionality reduction and clustering. In this paper we determine the pointwise limit of three different graph Laplacians used in the literature as the sample size increases and the neighborhood size approaches zero. We show that for a uniform measure on the submanifold all graph Laplacians have the same limit up to constants. However in the case of a non-uniform measure on the submanifold only the so called random walk graph Laplacian converges to the weighted Laplace-Beltrami operator.

The Gestalt laws (Wertheimer 1923) are widely regarded as the rules that help us parse the world into objects. However, it is unclear as to how these laws are acquired by an infant's visual system. Classically, these “laws” have been presumed to be innate (Kellman and Spelke 1983). But, more recent work in infant development, showing the protracted time-course over which these grouping principles emerge (e.g., Johnson and Aslin 1995; Craton 1996), suggests that visual experience might play a role in their genesis. Specifically, our studies of patients with late-onset vision (Project Prakash; VSS 2006) and evidence from infant development both point to an early role of common motion cues for object grouping. Here we explore the possibility that the privileged status of motion in the developmental timeline is not happenstance, but rather serves to bootstrap the learning of static Gestalt cues. Our approach involves computational analyses of real-world motion sequences to investigate whether primitive optic flow information is correlated with static figural cues that could eventually come to serve as proxies for grouping in the form of Gestalt principles.
We calculated local optic flow maps and then examined how similarity of motion across image patches co-varied with similarity of certain figural properties in static frames. Results indicate that patches with similar motion are much more likely to have similar luminance, color, and orientation as compared to patches with dissimilar motion vectors. This regularity suggests that, in principle, common motion extracted from dynamic visual experience can provide enough information to bootstrap region grouping based on luminance and color and contour continuation mechanisms in static scenes. These observations, coupled with the cited experimental studies, lend credence to the hypothesis that static Gestalt laws might be learned through a bootstrapping process based on early dynamic experience.

BACKGROUND AND PURPOSE: Digital subtraction angiography (DSA) is the method of reference for imaging of dural arteriovenous fistula (DAVF). The goal of this study was to analyze the value of different MR images including 3D contrast-enhanced MR angiography (MRA) with a high temporal resolution in diagnostic and follow-up imaging of DAVFs.
MATERIALS AND METHODS: A total of 18 MR/MRA examinations from 14 patients with untreated (n = 9) and/or treated (n = 9) DAVFs were evaluated. Two observers assessed all MR and MRA investigations for signs indicating the presence of a DAVF, for fistula characteristics such as fistula grading, location of fistulous point, and fistula obliteration after treatment. All results were compared with DSA findings.
RESULTS: On time-resolved 3D contrast-enhanced (TR 3D) MRA, the side and presence of all patent fistulas (n = 13) were correctly indicated, and no false-positive findings were observed in occluded DAVFs (n = 5). Grading of fistulas with this imaging technique was correct in 77% and 85% of patent fistulas for both readers, respectively. On T2-weighted images, signs indicative of a DAVF were encountered only in fistulas with cortical venous reflux (56%), whereas on 3D time-of-flight (TOF) MRA, most fistulas (88%) were correctly detected. In complete fistula occlusion, false-positive findings were encountered on both T2-weighted images and on TOF MRA images.
CONCLUSION: In this study, TR 3D MRA proved reliable in detecting DAVFs and suitable for follow-up imaging. The technique allowed—within limitations—to grade DAVFs. Although 3D TOF MRA can depict signs of DAVFs, its value for follow-up imaging is limited.

A Bayesian framework is developed to reconstruct the density of states from multiple canonical simulations. The framework encompasses the histogram reweighting method of Ferrenberg and Swendsen. The new approach applies to nonparametric as well as parametric models and does not require simulation data to be discretized. It offers a means to assess the precision of the reconstructed density of states and of derived thermodynamic quantities.

Motivation: Despite many years of research on how to properly align sequences in the presence of sequencing errors, alternative splicing and micro-exons, the correct alignment of mRNA sequences to genomic DNA is still a challenging task.
Results: We present a novel approach based on large margin learning that combines accurate plice site predictions with common sequence alignment techniques. By solving a convex optimization problem, our algorithm  called PALMA  tunes the parameters of the model such that true alignments score higher than other alignments. We study the accuracy of alignments of mRNAs containing artificially generated micro-exons to genomic DNA. In a carefully designed experiment, we show that our algorithm accurately identifies the intron boundaries as well as boundaries of the optimal local alignment. It outperforms all other methods: for 5702 artificially shortened EST sequences from C. elegans and human it correctly identifies the intron boundaries in all except two cases. The best other method is a recently proposed method called exalin which misaligns 37 of the sequences. Our method also demonstrates robustness to mutations, insertions and deletions, retaining accuracy even at high noise levels.
Availability: Datasets for training, evaluation and testing, additional results and a stand-alone alignment tool implemented in C++ and python are available at http://www.fml.mpg.de/raetsch/projects/palma.

To investigate the functional role of the striatum in visuo-motor adaptation, we extend the DIRECT-model for visuo-motor reaching movements formulated by Bullock et al.(J Cogn Neurosci 5:408435,1993) through two parallel loops, each modeling a distinct contribution of the corticocerebellarthalamocortical and the corticostriatothalamocortical networks to visuo-motor adaptation. Based on evidence of Robertson and Miall(Neuroreport 10(5): 10291034, 1999), we implement the function of the corticocerebellarthalamocortical loop as a module that gradually adapts to small changes in sensorimotor relationships. The corticostriatothalamocortical loop on the other hand is hypothesized to act as an adaptive search element, guessing new sensorimotor-transformations and reinforcing successful guesses while punishing unsuccessful ones. In a first step, we show that the model reproduces trajectories and error curves of healthy subjects in a two dimensional center-out reaching task with rotated screen cursor visual feedback. In a second step, we disable learning processes in the corticostriato thalamocortical loop to simulate subjects with Parkinsons disease (PD), and show that this leads to error curves typical of subjects with PD. We conclude that the results support our hypothesis, i.e., that the role of the corticostriatothalamocortical loop in visuo-motor adaptation is that of an adaptive search element.

In this paper, we propose a new method for FECG detection in abdominal recordings. The method consists of a sequential analysis approach, in which the a priori information about the interference signals is used for the detection of the FECG. Our method is evaluated on a set of 20 abdominal recordings from pregnant women with different gestational ages. Its performance in terms of fetal heart rate (FHR) detection success is compared with that of independent component analysis (ICA). The results show that our sequential estimation method outperforms ICA with a FHR detection rate of 85% versus 60% of ICA. The superior performance of our method is especially evident in recordings with a low signal-to-noise ratio (SNR). This indicates that our method is more robust than ICA for FECG detection.

The support vector machine (SVM) has been spotlighted in the machine learning community because of its theoretical soundness and practical performance. When applied to a large data set, however, it requires a large memory and a long time for training. To cope with the practical difficulty, we propose a pattern selection algorithm based on neighborhood properties. The idea is to select only the patterns that are likely to be located near the decision boundary. Those patterns are expected to be more informative than the randomly selected patterns. The experimental results provide promising evidence that it is possible to successfully employ the proposed algorithm ahead of SVM training.

Most literature on Support Vector Machines (SVMs) concentrate on
the dual optimization problem. In this paper, we would like to point out that the primal problem can also be solved efficiently, both for linear and non-linear SVMs, and that there is no reason for ignoring this possibilty.
On the contrary, from the primal point of view new families of algorithms for large scale SVM training can be investigated.

While kernel canonical correlation analysis (CCA) has been applied in many contexts, the convergence of finite sample estimates of the associated functions to their population counterparts has not yet been established. This paper gives a mathematical proof of the statistical convergence of kernel CCA, providing a theoretical justification for the method. The proof uses covariance operators defined on reproducing kernel Hilbert spaces, and analyzes the convergence of their empirical estimates of finite rank to their population counterparts, which can have infinite rank. The result also gives a sufficient condition for convergence on the regularization coefficient involved in kernel CCA: this should decrease as n^{-1/3}, where n is the number of data.

The pedestal or dipper effect is the large improvement in the detectability of a sinusoidal grating observed when it is added
to a masking or pedestal grating of the same spatial frequency, orientation, and phase. We measured the pedestal effect
in both broadband and notched noiseVnoise from which a 1.5-octave band centered on the signal frequency had been
removed. Although the pedestal effect persists in broadband noise, it almost disappears in the notched noise. Furthermore,
the pedestal effect is substantial when either high- or low-pass masking noise is used. We conclude that the pedestal effect
in the absence of notched noise results principally from the use of information derived from channels with peak sensitivities
at spatial frequencies different from that of the signal and the pedestal. We speculate that the spatial-frequency components
of the notched noise above and below the spatial frequency of the signal and the pedestal prevent off-frequency looking,
that is, prevent the use of information about changes in contrast carried in channels tuned to spatial frequencies that are
very much different from that of the signal and the pedestal. Thus, the pedestal or dipper effect measured without notched
noise appears not to be a characteristic of individual spatial-frequency-tuned channels.

Relative depth judgments of vertical lines based on horizontal disparity deteriorate enormously when the lines form part of closed configurations (Westheimer, 1979). In studies showing this effect, perspective was not manipulated and thus produced inconsistency between horizontal disparity and perspective. We show that stereoacuity improves dramatically when perspective and horizontal disparity are made consistent. Observers appear to use unhelpful perspective cues in judging the relative depth of the vertical sides of rectangles in a way not incompatible with a form of cue weighting. However, 95% confidence intervals for the weights derived for cues usually exceed the a-priori [0-1] range.

Article describes a neuromotor prosthesis (NMP), in development at Brown University, that records human brain signals, decodes them, and transforms them into movement commands. An NMP is described as a system consisting of a neural interface, a decoding system, and a user interface, also called an effector; a closed-loop system would be completed by a feedback signal from the effector to the brain. The interface is based on neural spiking, a source of information-rich, rapid, complex control signals from the nervous system. The NMP described, named BrainGate, consists of a match-head sized platform with 100 thread-thin electrodes implanted just into the surface of the motor cortex where commands to move the hand emanate. Neural signals are decoded by a rack of computers that displays the resultant output as the motion of a cursor on a computer monitor. While computer cursor motion represents a form of virtual device control, this same command signal could be routed to a device to command motion of paralyzed muscles or the actions of prosthetic limbs. The researchers’ overall goal is the development of a fully implantable, wireless multi-neuron sensor for broad research, neural prosthetic, and human neurodiagnostic applications.

Blind separation of convolutive mixtures by minimizing the mutual information between output sequences can avoid the side effect of temporally whitening the outputs, but it involves the score function difference, whose estimation may
be problematic when the data dimension is greater than two.
This greatly limits the application of this method. Fortunately, for separating convolutive mixtures, pairwise independence of outputs leads to their mutual independence. As an implementation of this idea, we propose a way to separate convolutive mixtures by enforcing pairwise independence. This approach can be applied to separate convolutive mixtures of a moderate number of sources.

Ring-resonators are in general not amenable to strain-free (non-contact) displacement measurements. We show that this limitation may be overcome if the ring-resonator, here a fiber-loop, is designed to contain a gap, such that the light traverses a free-space part between two aligned waveguide ends. Displacements are determined with nanometer sensitivity by measuring the associated changes in the resonance frequencies. Miniaturization should increase the sensitivity of the ring-resonator interferometer. Ring geometries that contain an optical circulator can be used to profile reflective samples. (c) 2006 Elsevier B.V. All rights reserved.

We show that magnetic-field-induced circular differential deflection of light can be observed in reflection or refraction at a single interface. The difference in the reflection or refraction angles between the two circular polarization components is a function of the magnetic-field strength and the Verdet constant, and permits the observation of the Faraday effect not via polarization rotation in transmission, but via changes in the propagation direction. Deflection measurements do not suffer from n-pi ambiguities and are shown to be another means to map magnetic fields with high axial resolution, or to determine the sign and magnitude of magnetic-field pulses in a single measurement.

In an optically active liquid the diffraction angle depends on the circular polarization state of the incident light beam. We report the observation of circular differential diffraction in an isotropic chiral medium, and we demonstrate that double diffraction is an alternate means to determine the handedness (enantiomeric excess) of a solution. (c) 2007 Optical Society of America.

HFSP Journal Frontiers of Interdisciplinary Research in the Life Sciences, 1(2):115-126, 2007, clmc (article)

Abstract

Research in robotics has moved away from its primary focus on industrial
applications. The New Robotics is a vision that has been developed in past years
by our own university and many other national and international research
instiutions and addresses how increasingly more human-like robots can live
among us and take over tasks where our current society has shortcomings. Elder
care, physical therapy, child education, search and rescue, and general
assistance in daily life situations are some of the examples that will benefit from
the New Robotics in the near future. With these goals in mind, research for the
New Robotics has to embrace a broad interdisciplinary approach, ranging from
traditional mathematical issues of robotics to novel issues in psychology,
neuroscience, and ethics. This paper outlines some of the important research
problems that will need to be resolved to make the New Robotics a reality.

We present an analysis of the spatial and temporal statistics of "natural" optical flow fields and a novel flow algorithm that exploits their spatial statistics. Training flow fields are constructed using range images of natural scenes and 3D camera motions recovered from hand-held and car-mounted video sequences. A detailed analysis of optical flow statistics in natural scenes is presented and machine learning methods are developed to learn a Markov random field model of optical flow. The prior probability of a flow field is formulated as a Field-of-Experts model that captures the spatial statistics in overlapping patches and is trained using contrastive divergence. This new optical flow prior is compared with previous robust priors and is incorporated into a recent, accurate algorithm for dense optical flow computation. Experiments with natural and synthetic sequences illustrate how the learned optical flow prior quantitatively improves flow accuracy and how it captures the rich spatial structure found in natural scene motion.

Our goal is to understand the principles of Perception, Action and Learning in autonomous systems that successfully interact with complex environments and to use this understanding to design future systems